Establishing an eyeball-weight relationship for Litopenaeus vannamei using machine vision technology

Abstract

This study aimed to establish a shrimp eyeball-weight relationship model for Litopenaeus vannamei using machine vision technology. A total of 295 shrimp were sampled from a recirculating aquaculture system (RAS). The long-axis length (d), body length (L), and body weight (W) of each individual was measured. The long axis length of the shrimp eyeball was identified and measured using machine vision technology. Continuous fitting and piecewise fitting models were used to construct the eyeball-weight relationship model for L. vannamei. The continuous fitting relationship model was described as: W = 38.865d2.7914, while the piecewise model was described as: d < 2 mm, W = 0.0326d3.7363, R² = 0.9288; 2 mm ≤ d < 3.9 mm, W = 0.0401d3.104, R² = 0.9629; 3.9 mm ≤ d < 5.8 mm, W = 0.0421d3.0311, R² = 0.9216; 5.8 mm < d, W = 0.103d2.6226, R² = 0.9457. The root mean square error (RMSE) of the piecewise fitting model (0.0244, 0.1575, 0.5034, 0.7072) was smaller than the continuous fitting model (0.8229). The correlation coefficient (R2) of the piecewise model (0.9288, 0.9629, 0.9216, and 0.9457) was similar to that of the continuous fitting model (R2 = 0.9621). The results indicated that the piecewise fitting model is suitable for calculating the biomass of L. vannamei in RAS and provides a novel way of estimating the biomass of L. vannamei cultured in RAS. The piecewise fitting model can also provide the foundation of evaluating the production of shrimp using underwater image recognition in intelligent aquaculture systems.